International Journal of Research and Innovation in Applied Science (IJRIAS)
A Study of E-Commerce Product Consumption Analysis and Visualization
Published July 11, 2026 • Vol. 11, Issue 6, pp. 2956–2962Open Access
DOI: 10.51584/IJRIAS.2026.11060221
Abstract
In the current paper researcher says that this review presents a comprehensive overview of many methodologies used in recent years to visualize, predict customer behavior and segment using data analysis and machine learning. It introduce new trends such as pictorial storytelling with data, AI driven personalization, real-time behavioral analysis and deep learning prediction models as fundamental influencers for the next generation of e-commerce platforms. The paper also suggest future research directions like hybrid model development (combine two or more methods), protecting user privacy, and adaptive visualization systems that can help businesses make better decisions and customer engagement in online retail ecosystems.
Keywords: E-Commerce Product Consumption Analysis, Machine Learning–Driven Visualization, Consumer Behavior Analytics, Interactive Visual Analytics, Real-Time Predictive Analysis, AI-Powered Decision Support
| Journal | International Journal of Research and Innovation in Applied Science (IJRIAS) |
|---|---|
| ISSN | 2454-6194 |
| Volume / Issue | Volume 11, Issue 6 |
| Pages | 2956–2962 |
| Publication date | July 11, 2026 |
| DOI | 10.51584/IJRIAS.2026.11060221 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Bhogesara Madhu M (2026). A Study of E-Commerce Product Consumption Analysis and Visualization. International Journal of Research and Innovation in Applied Science (IJRIAS), 11(6), 2956-2962. https://doi.org/10.51584/IJRIAS.2026.11060221
BibTeX
@article{Bhogesara2026,
title = {A Study of E-Commerce Product Consumption Analysis and Visualization},
author = {Bhogesara Madhu M},
journal = {International Journal of Research and Innovation in Applied Science (IJRIAS)},
volume = {11},
number = {6},
pages = {2956--2962},
year = {2026},
doi = {10.51584/IJRIAS.2026.11060221},
publisher = {RSIS International}
}